The Dominance Concept Inventory: A Tool for Assessing Undergraduate Student Alternative Conceptions about Dominance in Mendelian and Population Genetics

Despite the impact of genetics on daily life, biology undergraduates understand some key genetics concepts poorly. One concept requiring attention is dominance, which many students understand as a fixed property of an allele or trait and regularly conflate with frequency in a population or selective advantage. We present the Dominance Concept Inventory (DCI), an instrument to gather data on selected alternative conceptions about dominance. During development of the 16-item test, we used expert surveys (n = 12), student interviews (n = 42), and field tests (n = 1763) from introductory and advanced biology undergraduates at public and private, majority- and minority-serving, 2- and 4-yr institutions in the United States. In the final field test across all subject populations (n = 709), item difficulty ranged from 0.08 to 0.84 (0.51 ± 0.049 SEM), while item discrimination ranged from 0.11 to 0.82 (0.50 ± 0.048 SEM). Internal reliability (Cronbach\u27s alpha) was 0.77, while test–retest reliability values were 0.74 (product moment correlation) and 0.77 (intraclass correlation). The prevalence of alternative conceptions in the field tests shows that introductory and advanced students retain confusion about dominance after instruction. All measures support the DCI as a useful instrument for measuring undergraduate biology student understanding and alternative conceptions about dominance

Iterative Design of a Simulation-Based Module for Teaching Evolution by Natural Selection

Background: This research builds on a previous study that looked at the effectiveness of a simulation-based module for teaching students about the process of evolution by natural selection. While the previous study showed that the module was successful in teaching how natural selection works, the research uncovered some weaknesses in the design. In this paper, we used design-based research to investigate how design changes to the module affected not only students’ understanding of the concepts but also their usage of misconceptions in the assessments. We present results from two studies. In study 1, we looked at gains in understanding on a pre and post-assessment for students who used the revised version of the module. We also examined misconception uses in their answer selections. In study 2, we compared the performance on a summative assessment between students who used the revised version and students who used the original version of the module. We also looked at misconception uses in their answer selections. Results: In study 1, we saw a significant improvement in the pre-post assessment for students who used the revised version. In study 2, we did not find a significant difference on the overall performance outcome between students who used the revised and those that used the original version of the module. In both studies, however, we saw a lower use of misconceptions after students used the revised module. In particular, we saw less use of the adaptive mutation misconception, the belief that mutations are adaptive responses to the environment and are biased towards advantageous mutations. This is promising because in the previous study there was no evidence of decreased use of this misconception. Conclusions: Students showed learning gains on all targeted key concepts, and reduced expression of all targeted misconceptions, which was not found previously for students using the older workbook version of the module. In particular, the revised version appears to help students overcome the adaptive mutation misconception. This article demonstrates how design-based research can contribute to the ongoing improvement of evidence-based instruction in undergraduate biology classrooms

A new assessment of graph construction competency for undergraduate biology students

With an increasing emphasis on teaching the skills and processes of science in the undergraduate biology classroom, working with and interpreting data has become an important part of the curriculum. Visual representations are a key tool when examining data, especially graphs. Undergraduate biology students notoriously have trouble both making good graphs and interpreting graphs. Yet, although there is an extensive literature on graph interpretation challenges amongst students, there has been much less work on the confusions students exhibit when constructing graphs. On the path to creating tools to help teach graphing to biology students, we have been building a new performance-based assessment of graph construction competence. The assessment presents students a research question and asks them to make graphs to test a hypothesis drawn from that question. The graphs are auto-scored for a number of practices associated with making good graphs. The digital nature and auto-scoring has allowed us to provide this assessment and analyze results at larger scales than previous assessments, gathering data that will help focus teaching tools on the areas of highest need. In this workshop, each participant will take one version of the graphing assessment themselves (about 20–30 minutes) and then we will discuss the experience. After talking about how well the assessment lines up to the graphing practices you look for in your students, the presenter will show data on where we find biology students struggle, drawn from students in a diverse set of classes and institutions. Bring your laptop (Mac or Windows only).Note: the creative commons license below is for the abstract and talk only, not the software

The Genetic Drift Inventory: A Tool for Measuring What Advanced Undergraduates Have Mastered about Genetic Drift

Understanding genetic drift is crucial for a comprehensive understanding of biology, yet it is difficult to learn because it combines the conceptual challenges of both evolution and randomness. To help assess strategies for teaching genetic drift, we have developed and evaluated the Genetic Drift Inventory (GeDI), a concept inventory that measures upper-division students’ understanding of this concept. We used an iterative approach that included extensive interviews and field tests involving 1723 students across five different undergraduate campuses. The GeDI consists of 22 agree–disagree statements that assess four key concepts and six misconceptions. Student scores ranged from 4/22 to 22/22. Statements ranged in mean difficulty from 0.29 to 0.80 and in discrimination from 0.09 to 0.46. The internal consistency, as measured with Cronbach\u27s alpha, ranged from 0.58 to 0.88 across five iterations. Test–retest analysis resulted in a coefficient of stability of 0.82. The true–false format means that the GeDI can test how well students grasp key concepts central to understanding genetic drift, while simultaneously testing for the presence of misconceptions that indicate an incomplete understanding of genetic drift. The insights gained from this testing will, over time, allow us to improve instruction about this key component of evolution

Analysis of the limitations in the oxygen reduction activity of transition metal oxide surfaces

The oxygen reduction reaction (ORR) is the key bottleneck in the performance of fuel cells. So far, the most active and stable electrocatalysts for the reaction are based on Pt group metals. Transition metal oxides (TMOs) constitute an alternative class of materials for achieving operational stability under oxidizing conditions. Unfortunately, TMOs are generally found to be less active than Pt. Here, we identify two reasons why it is difficult to find TMOs with a high ORR activity. The first is that TMO surfaces consistently bind oxygen atoms more weakly than transition metals do. This makes the breaking of the O–O bond rate-determining for the broad range of TMO surfaces investigated here. The second is that electric field effects are stronger at TMO surfaces, which further makes O–O bond breaking difficult. To validate the predictions and ascertain their generalizability for TMOs, we report experimental ORR catalyst screening for 7,798 unique TMO compositions that generally exhibit activity well below that of Pt

A Community-Building Framework for Collaborative Research Coordination across the Education and Biology Research Disciplines

Since 2009, the U.S. National Science Foundation Directorate for Biological Sciences has funded Research Coordination Networks (RCN) aimed at collaborative efforts to improve participation, learning, and assessment in undergraduate biology education (UBE). RCN-UBE projects focus on coordination and communication among scientists and educators who are fostering improved and innovative approaches to biology education. When faculty members collaborate with the overarching goal of advancing undergraduate biology education, there is a need to optimize collaboration between participants in order to deeply integrate the knowledge across disciplinary boundaries. In this essay we propose a novel guiding framework for bringing colleagues together to advance knowledge and its integration across disciplines, the “Five ‘C’s’ of Collaboration: Commitment, Collegiality, Communication, Consensus, and Continuity.” This guiding framework for professional network practice is informed by both relevant literature and empirical evidence from community-building experience within the RCN-UBE Advancing Competencies in Experimentation–Biology (ACE-Bio) Network. The framework is presented with practical examples to illustrate how it might be used to enhance collaboration between new and existing participants in the ACE-Bio Network as well as within other interdisciplinary networks

The Basic Competencies of Biological Experimentation: Concept-Skill Statements

This biological experimentation competencies map is a model created by members of the ACE-Bio Network of seven areas a competent biologist calls in when doing experimentation in biology. Each competency is represented by a summary word on a uniquely colored segment of the model. For presentation convenience, the seven major areas within experimentation in biology are mapped onto tables in a linear manner. However, this is not meant to convey a particular order that one must follow during experimentation. The areas are given equal weight and flexible order of their use throughout the process of experimentation. This work is meant to provide a framework for ACE Bio Network participants and other instructors or academic leaders in the biological sciences to study implementation of experimentation activities and assessments across diverse institutional and curricular contexts. In addition to the document in pdf format, another link provides the file in MSWord format so that users can easily modify it to guide assessment of student learning about experimentation, undergraduate biology instruction, curriculum development, professional faculty development, program evaluation, or review of research literature in a way that is appropriate to their own context

Propofol induces MAPK/ERK cascade dependant expression of cFos and Egr-1 in rat hippocampal slices

Background: Propofol is a commonly used intravenous anesthetic agent, which produce rapid induction of and recovery from general anesthesia. Numerous clinical studies reported that propofol can potentially cause amnesia and memory loss in human subjects. The underlying mechanism for this memory loss is unclear but may potentially be related to the induction of memory-associated genes such as c-Fos and Egr-1 by propofol. This study explored the effects of propofol on c-Fos and Egr-1 expression in rat hippocampal slices. Findings: Hippocampal brain slices were exposed to varying concentrations of propofol at multiple time intervals. The transcription of the immediate early genes, c-Fos and Egr-1, was quantified using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). MAPK/ERK inhibitors were used to investigate the mechanism of action. We demonstrate that propofol induced the expression of c-Fos and Egr-1 within 30 and 60 min of exposure time. At 16.8 μM concentration, propofol induced a 110% increase in c-Fos transcription and 90% decrease in the transcription of Egr-1. However, at concentrations above 100 μM, propofol failed to induce expression of c-Fos but did completely inhibit the transcription of Egr-1. Propofol-induced c-Fos and Egr-1 transcription was abolished by inhibitors of RAS, RAF, MEK, ERK and p38-MAPK in the MAPK/ERK cascade. Conclusions: Our study shows that clinically relevant concentrations of propofol induce c-Fos and down regulated Egr-1 expression via an MAPK/ERK mediated pathway. We demonstrated that propofol induces a time and dose dependant transcription of IEGs c-Fos and Egr-1 in rat hippocampal slices. We further demonstrate for the first time that propofol induced IEG expression was mediated via a MAPK/ERK dependant pathway. These novel findings provide a new avenue to investigate transcription-dependant mechanisms and suggest a parallel pathway of action with an unclear role in the activity of general anesthetics

Modern computing: vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Biomarkers of coagulation, endothelial function, and fibrinolysis in critically ill patients with COVID-19: A single-center prospective longitudinal study

Background: Immunothrombosis and coagulopathy in the lung microvasculature may lead to lung injury and disease progression in coronavirus disease 2019 (COVID-19). We aim to identify biomarkers of coagulation, endothelial function, and fibrinolysis that are associated with disease severity and may have prognostic potential. Methods: We performed a single-center prospective study of 14 adult COVID-19(+) intensive care unit patients who were age- and sex-matched to 14 COVID-19(−) intensive care unit patients, and healthy controls. Daily blood draws, clinical data, and patient characteristics were collected. Baseline values for 10 biomarkers of interest were compared between the three groups, and visualized using Fisher\u27s linear discriminant function. Linear repeated-measures mixed models were used to screen biomarkers for associations with mortality. Selected biomarkers were further explored and entered into an unsupervised longitudinal clustering machine learning algorithm to identify trends and targets that may be used for future predictive modelling efforts. Results: Elevated D-dimer was the strongest contributor in distinguishing COVID-19 status; however, D-dimer was not associated with survival. Variable selection identified clot lysis time, and antigen levels of soluble thrombomodulin (sTM), plasminogen activator inhibitor-1 (PAI-1), and plasminogen as biomarkers associated with death. Longitudinal multivariate k-means clustering on these biomarkers alone identified two clusters of COVID-19(+) patients: low (30%) and high (100%) mortality groups. Biomarker trajectories that characterized the high mortality cluster were higher clot lysis times (inhibited fibrinolysis), higher sTM and PAI-1 levels, and lower plasminogen levels. Conclusions: Longitudinal trajectories of clot lysis time, sTM, PAI-1, and plasminogen may have predictive ability for mortality in COVID-19